Methods and Systems for Improving a Search Ranking Using Population Information

ABSTRACT

Systems and methods that improve search rankings for a search query by using data associated with queries related to the search query are described. In one aspect, a search query is received, a population associated with the search query is determined, an article (such as a webpage) associated with the search query is determined, and a ranking score for the article based at least in part on data associated with the population is determined. Algorithms and types of data associated with a population useful in carrying out such systems and methods are described.

FIELD OF THE INVENTION

The invention generally relates to search engines. More particularly,the invention, relates to methods-and systems for improving a searchranking using population information.

BACKGROUND OF THE INVENTION

Conventional search engines operating in a networked computerenvironment such as the World Wide Web or in an individual computercan-provide search results in response to entry of a user's searchquery. In many instances, the search results are ranked in accordancewith the search engine's scoring or ranking system or method. Forexample, conventional search engines score or rank documents of a searchresult for a particular query by the number of times a keyword orparticular word or phrase appears in each document in the searchresults. Documents include, for example, web pages of various formats,such as HTML, XML, XHTML; Portable Document Format (PDF) files; and wordprocessor and application program document files. Other search enginesbase scoring or ranking results on more than the content of thedocument. For example, one known method, described in an articleentitled “The Anatomy of a Large-Scale Hypertextual Search Engine,” bySergey Brin and Lawrence Page, assigns a degree of importance to adocument, such as a web page, based on the link structure of the webpage. Other conventional methods involve selling a higher score or rankin search results for a particular query to third parties that want toattract users or customers to their websites.

In some instances, a user in a particular location may enter a searchquery in a search engine to obtain search results relevant to the user.For example, a user in Japan may enter a search query to obtain searchresults that include Japanese language websites. In response to suchqueries, conventional search engines can return unreliable searchresults since there is relatively little data to rank or score searchresults according to the user's location that are relevant or useful tothe user for the search query.

Conventional search engines can determine location informationassociated with a user from the type of web browser application used toaccess the search engine. For example, when a user downloads a webbrowser application from the Internet, the user may have the option todownload a particular version of the application depending upon theuser's preferred language, e.g. Japanese or French versions. When a useruses the French version of a web browser application to access a searchengine via the Internet, the search engine can often determine that theuser is likely located in France merely by detecting use of the Frenchversion of the web browser application.

Other conventional search engines obtain location information by thecountry domain suffix a particular user used in a search query. Forexample, a Japanese user requesting the Japanese version of a searchengine may input the web address for the search engine with the countrydomain suffix of “co.jp” instead of the domain name suffix “.com.” Basedon such input, a search engine could determine that the user is likelylocated in Japan.

If a search engine returns more than one search result in response to asearch query, the search results may be displayed as a list of links tothe documents associated with the search results. A user may browse andvisit a website associated with one or more of the search results toevaluate whether the website is relevant to the user's search query. Forexample, a user may manipulate a mouse or another input device and“click” on a link to a particular search result to view a websiteassociated with the search result. In many instances, the user willbrowse and visit several websites provided in the search result,clicking on links associated with each of the several websites to accessvarious websites associated with the search results before locatinguseful or relevant information to address the user's search query.

Clicking on multiple links to multiple websites associated with a singleset of search results can be time consuming. It is desireable to improvethe ranking algorithm used by search engines and to therefore provideusers with better search results.

SUMMARY

Embodiments of the present invention comprise systems and methods thatimprove search rankings for a search query by using populationinformation associated with the search query are described. One aspectof the present invention comprises receiving a search query, anddetermining a population associated with the search query. Suchpopulations may be defined and determined in a variety of ways. Anotheraspect of an embodiment of the present invention comprises determiningan article (such as a webpage) associated with the search query, anddetermining a ranking score for the article based at least in part ondata associated with the population. A variety of algorithms usingpopulation information may be applied in such systems and methods.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentinvention are better understood when the following Detailed Descriptionis read with reference to the accompanying drawings, wherein:

FIG. 1 illustrates a block diagram of a system in accordance with oneembodiment of the present invention;

FIG. 2 illustrates a flow diagram of a method in accordance with oneembodiment of the present invention; and

FIG. 3 illustrates a flow diagram of a subroutine of the method shown inFIG. 2.

DETAILED DESCRIPTION

The present invention comprises methods and systems for improving asearch ranking by using population information. Reference will now bemade in detail to exemplary embodiments of the invention as illustratedin the text and accompanying drawings. The same reference numbers areused throughout the drawings and the following description to refer tothe same or like parts.

Various systems in accordance with the present invention may beconstructed. FIG. 1 is a diagram illustrating an exemplary system inwhich exemplary embodiments of the present invention may operate. Thepresent invention may operate in, and be embodied in, other systems aswell.

The system 100 shown in FIG. 1 includes multiple client devices 102 a-n,a server device 104, and a network 106. The network 106 shown includesthe Internet. In other embodiments, other networks, such as an intranetmay be used. Moreover, methods according to the present invention mayoperate in a single computer. The client devices 102 a-n shown eachinclude a computer-readable medium, such as a random access memory (RAM)108, in the embodiment shown coupled to a processor 110. The processor110 executes a set of computer-executable program instructions stored inmemory 108. Such processors may include a microprocessor, an ASIC, andstate machines. Such processors include, or may be in communicationwith, media, for example, computer-readable media, which storesinstructions that, when executed by the processor, cause the processorto perform the steps described herein. Embodiments of computer-readablemedia include, but ate not limited to, an electronic, optical, magnetic,or other storage or transmission device capable of providing aprocessor, such as the processor in communication with a touch-sensitiveinput device, with computer-readable instructions. Other examples ofsuitable media include, but are not limited to, a floppy-disk, CD-ROM,magnetic disk, memory chip, ROM, RAM, an ASIC, a configured processor,all optical media, all magnetic tape or other magnetic media, or anyother medium from which a computer processor can read instructions. Alsovarious other forms of computer-readable media may transmit or carryinstructions to a computer, including a router, private or publicnetwork, or other transmission device or channel, both wired andwireless. The instructions may comprise code from anycomputer-programming language, including, for example, C, C++, C#,Visual Basic, Java, and JavaScript.

Client devices 102 a-n may also include a number of external or internaldevices such as a mouse, a CD-ROM, a keyboard, a display, or other inputor output devices. Examples of client devices 102 a-n are personalcomputers, digital assistants, personal digital assistants, cellularphones, mobile phones, smart phones, pagers, digital tablets, laptopcomputers, a processor-based device and similar types of systems anddevices. In general, a client device 102 a-n may be any type ofprocessor-based platform connected to a network 106 and that interactswith one or more application programs. The client devices 102 a-n showninclude personal computers executing a browser application program suchas Internet Explorer™, version 6.0 from Microsoft Corporation; NetscapeNavigator™, version 7.1 from Netscape Communications Corporation; andSafari™, version 1.0 from Apple Computer.

Through the client devices 102 a-n, users 112 a-n can communicate overthe network 106 with each other and with other systems and devicescoupled to the network 106. Users 112 a-n can be located in differentlocations, countries, or regions. As shown in FIG. 1, a server device104 is also coupled to the network 106. In the embodiment shown, a user112 a-n can generate a search query 114 at a client device 102 a-n totransmit to the server device 104 via the network 106. For example, auser 112 a in one country types a textual search query 114 into a queryfield of a web page displayed on the client device 102 a. The clientdevice 102 a then transmits an associated search query signal 126reflecting the search query 114 via the network 106 to the server device104.

The server device 104 shown includes a server executing a search engineapplication program such as the Google™ search engine. Similar to theclient devices 102 a-n, the server device 104 shown includes a processor116 coupled to a computer readable memory 118. Server device 104,depicted as a single computer system, may be implemented as a network ofcomputer processors. Examples of a server device 104 are servers,mainframe computers, networked computers, a processor-based device andsimilar types of systems and devices. Client processors 110 and theserver processor 116 can be any of a number of Well-known computerprocessors, such as processors from Intel Corporation of Santa Clara,Calif.; and Motorola Corporation of Schaumburg, Ill.

Memory 118 contains the search engine application program, also known asa search engine 124. The search engine 124 locates relevant informationin response to a search query 114 from a user 112 a-n.

The server device 104, or related device, has previously performed asearch of the network 106 to locate articles, such as web pages, storedat other devices or systems connected to the network 106, and indexedthe articles in memory 118 or another data storage device. Articlesinclude, documents, for example, web pages of various formats, such asHTML, XML, XHTML, Portable Document Format (PDF) files, and wordprocessor, database, and application program document files, audio,video, or any other information of any type whatsoever made available ona network (such as the Internet), a personal computer, or othercomputing or storage means. The embodiments described herein aredescribed generally in relation to documents, but embodiments mayoperate on any type of article.

The search engine 124 responds lo the associated search query signal 126reflecting the search query 114 by returning a set of relevantinformation or search results 132 to client device 102 a-n from whichthe search query 114 originated.

The search engine 124 shown includes a document locator 134, a rankingprocessor 136, and a population processor 138. In the embodiment shown,each comprises computer code residing in the memory 118. The documentlocator 134 identifies a set of documents that are responsive to thesearch query 114 from a user 112 a. In the embodiment shown, this isaccomplished by accessing an index of documents, indexed in accordancewith potential search queries or search terms. The ranking processor 136ranks or scores the search result 132 including the located set of webpages or documents based upon relevance lo a search query 114 and/or anyanother criteria. The population processor 138 determines or otherwisemeasures a population signal such as a population signal 128 thatreflects or otherwise corresponds to a population associated with a user112 a-n. Note that other functions and characteristics of the documentlocator 134, ranking processor 136, and population processor 138 arefurther described below.

Server device 104 also provides access to other storage elements, suchas a population data storage element, in the example shown a populationdatabase 120, and a selection data storage element, in the exampleshown, a selection data database 122. The specific selection databaseshown is a clickthrough database, but any selection data storage elementmay be used. Data storage elements may include any one or combination ofmethods for storing data, including without limitation, arrays,hashtables, lists, and pairs. Other similar types of data storagedevices can be accessed by the server device 104. The populationdatabase 120 stores population information associated with users 112 a-ninputting search queries. Examples of population information associatedwith users 112 a-n includes information about the locations of users 112a-n, information about the populations with which users 112 a 0 n areassociated, and information about groups with which users 112 a-n areassociated.

Examples of locations of users can include, but are not limited to, acontinent, a region, a country, a state, a county, or a city. By way ofexample, locations of users can be identified by country, such asFrance, Germany, Japan, and the United States.

Examples of populations with which users are associated can include, butare not limited to, a gender, a demographic, an ethnicity, a continent,a region, a country, a state, a county, or a city. By way of example,populations with which users are associated with can be identified byage ranges of the user, such as “under 18 years old,” “18-24 years old,”“25-34 years old,” “35-49 years old,” “50-62 years old,” and “over 62years old.”

Examples of groups with which users are associated, can include, but arenot limited to, a gender, a demographic, group, an ethnic group, personswith a shared characteristic, persons with a shared interest, andpersons grouped by a predetermined selection. By way of example, groupswith which users can be associated with can be identified as “allpersons interested in collecting ancient shark teeth,” and “all personsnot interested in collecting ancient shark teeth.”

Population information can also include self identification-type data orautomatic identification-type data. Self identification-type dataincludes, but is not limited to, user registration data, User preferencedata, and other user selected data. By way of example,self-identification data is a language preference selection that a userinputs into a browser application program. Automatic identification-typedata includes, but is not limited to, the Internet protocol address of auser's location, default data obtained from a user's browser applicationprogram, cookies, and other data collected from a user's applicationprogram when the user's application program interacts with a searchengine. By way of example, automatic-identification data may comprisethe domain of a user's network address on the Internet, or may beinformation stored in a “cookie” obtained by or accessed by a user'sbrowser application program.

The search engine 124 determines population information or otherwiseexecutes a set of instructions to determine population informationassociated with users 112 a-n, and stores population-type information inthe population database 120. Alternatively, the population processor 138determines population information or otherwise executes a set ofinstructions to determine population information associated with users112 a-n, and stores population-type information in the populationdatabase 120.

It should be noted that the present invention may comprise systemshaving different architecture than that which is shown in FIG. 1. Forexample, in some systems according to the present invention, thepopulation database 120 and processor 138 may not be pan of the searchengine 124, and may carry out modification of population data or otheroperations offline. Also, in other embodiments, the population processor138 may affect the output of the document locator 134 or other system.The system 100 shown in FIG. 1 is merely exemplary, and is used toexplain the exemplary methods shown in FIGS. 2-3.

In the embodiment shown, the population database 120 contains datagathered and stored prior to carrying out the example method of thepresent invention as shown in FIGS. 2-3. Still referring to FIG. 1, thepopulation processor 138 shown records population information associatedwith users 112 a-n by obtaining and analyzing the location of a userinputting a search query and selecting search results for the searchquery. For example, when a user 112 a in Japan types in a first searchquery such as “boating,” the population processor 138 determines thatthe user 112 a is transmitting the search query from an Internetprotocol address located in Japan, and is also using Japanese languagepreferences for the browser application program. Furthermore, when theuser 112 a selects particular search results returned by the searchengine in response to the search query, the population processor 138determines that the user 112 a selects particular documents in theJapanese language. Thus, when a user 112 a in Japan inputs the query“boating,” search results relevant to the user 112 a in Japan may bereturned such as “boating.co.jp.” Other types of population informationcan be determined by the invention and stored by the population database120.

By way of another example, the population database 120 can storeinformation that a user is associated with sub-populations of apopulation. For example, the population processor 138 determines that auser 112 a in Europe, a region in the world, is from Luxembourg, acountry in Europe. The population database 120 can also storeinformation that another user in Europe is from France, another countryin Europe. Thus, while each user is associated with the population of“Europe,” each user is associated with a respective sub-population,“Luxembourg” and “France.” Populations and sub-populations can include,but are not limited to, continents, regions, countries, states,counties, cities, genders, demographic groups, ethnic groups, languagesspoken, universal resource locators, internet protocol addresses, domainnames, internet service providers, groups, persons with sharedcharacteristics, persons with shared interests, and persons grouped by apredetermined selection. Various levels of sub-populations can exist fora population. For example, “Parisians” are a sub-population to “France”which is a sub-population to “Europe” which is a sub-population to the“World.” Subpopulation information can be useful if there is aninsufficient number of user clicks from users from a particular locationor population such as France. However, since France is a sub-populationof “Europe,” click information corresponding to users in “Europe” couldbe used to augment the click information for a query from the user inFrance. Generally, if click information for a sub-population is sparseor does not exist, information from a higher population level can beused to augment the click information.

In determining population and sub-population information, the populationprocessor 138 can also determine a weight for each type of information.For example, the population processor 138 can determine to weight that auser is from a particular region less than the weight for informationthat a user is from a particular country so that improved search resultsfor subsequent search queries can be obtained from region and countryinformation. Thus, information that a user is from a particular region(Europe) can be weighted less than information that a user is from aparticular country (France). Other types of weighting or similar,population-type data can be defined by the invention and stored by thestored by a population database 120.

The population database 120 shown includes a list of user locations fora particular query. For example, for the search query “boating,”population information such as the determined location of users whoinput the query “boating” are stored and associated with the searchquery “boating.” The user's locations can be “France,” “Japan,” and the“United States.” These locations are used for example purposes. In otherembodiments, the number of locations can be greater or fewer, or othercountries, locations, populations, or sub-populations can be used.

An example of information stored in a population database implemented byvarious embodiments of the invention is as follows:

Query Locations Q1 Japan, France, United States Q2 Europe, Asia, NorthAmerica

In the table shown above, the first column lists unique queries and thesecond column lists corresponding locations of users. Each queryrepresents a search query input by a user. The corresponding locationsof users represents the determined location of users who input therespective query. Thus, in for query “Q1” shown above, determinedlocations of users who previously input the query “Q1” includes “Japan,”“France,” and the “United States.” When the table is implemented by theinvention, the search engine 124 can call upon the determined locationsof users in the “Location” column for a particular query such as “Q1.”When a new query is input by a user, the new query is inserted into the“Query” column. Likewise, when a location of a user is determined forthe new query, the new location is inserted into the second columntitled “Location.”

The click through database 122 shown stores data associated with users'selection of a search result 132 from a search engine 124, such as froma list of documents located in response to a search query 114. Forexample, a user 112 a enters an input at a client device 102 a-n, suchas manipulating a mouse or another input device to click on one or moreURLs associated with web pages or documents of a search result 132. Auser “click” is generated by the user's selection of a document locatedin the search result 132. This “click” on a document is stored in theclickthrough database 122 as a selection associated with the document'spresence in a search result returned for a particular search query. Manyother such selection-type data, associated with users' selections ofdocuments in search results, are stored there as well.

User clicks are referred to as “clickthrough” data. In the embodimentshown, the search engine 124 measures and stores the clickthrough dataas well as other data related to each of the-documents located in thesearch result 132.

Clickthrough data is generally an indicator of quality in a searchresult. Quality signals or clickthrough data can include, but is notlimited to, whether a particular URL or document is clicked by aparticular user; how often a URL, document, or web page is clicked byone or more users; and how often a particular user clicks on specificdocuments or Web pages. Other types of quality signals similar toclickthrough data, such as user inputs or observational type data, canbe stored by a clickthrough database 122 or similar data storagedevices.

Other data related to documents located in a search result 132 that canbe stored in a clickthrough database 122 or other data storage devicecan include, but is not limited to, how often a particular URL,document, or web page is shown in response to a search query 114; howmany times a particular search query 114 is asked by users 112 a-n froma particular location; how many times a particular search query 114 isasked by users 112 a-n from a particular population; how many times aparticular document is selected by users 112 a-n from a particularlocation, how many times a particular document is selected by users 112a-n from a particular population; how many times a particular documentis by selected by users 112 a-n for a particular search query 114; theage or time a particular document has been posted on a network 106, andidentity of a source of a particular document on a network 106.

Population information from the population database 120 and selectiondata from the selection database (shown as a clickthrough database 122)can be processed by the population processor 138 and stored forsubsequent use. For example, the population processor 138 retrievesclickthrough data for a particular search query. The clickthrough datafor the particular search query is apportioned based on users'locations. The search engine 124 calls to the population database 120for location information for all users entering a particular searchquery and selecting documents for the search result for the query. Ifthe population processor 138 determines that users from three locations,Japan, France, and the United States, submitted selection data for aparticular query 114, a respective designation for each set of usersfrom each location can be defined by the population processor 138. Thus,users from Japan can be designated as “J,” users from France can bedesignated as “F,” and users from the United States can be designated as“US.” The population processor 138 then apportions the number of clickscollected by the clickthrough database 122 for the particular set ofdocuments to each respective designation based on user location.

One example of information stored in a population database implementedby an embodiment of the invention is as follows:

Total Number of Query Document All Clicks Japan France United States QD₁ 101 1 20 80 Q D₂ 207 2 5 200 . . . . . . . . . . . . . . . . . . QD_(i) #(Q, D_(i), A) #(Q, D_(i), J) #(Q, D_(i), F) #(Q, D_(i), U) . . .. . . . . . . . . . . . . . . Q D_(N) #(Q, D_(N), A) #(Q, D_(N), J) #(Q,D_(N), F) #(Q, D_(N), U) Q D_(total) 1500 100 300 1100

In the example provided above for the population database 120 shown, forthe search query “Q,” the total number of user clicks on document “D₁”was “101.” The total number of user clicks on document “D₁” by userslocated in Japan was “1,” the total number of user clicks on document“D₁” by users located in France was “20,” and the total number of userclicks on document “D₁” by users located in the United States was “80.”

In the embodiment shown in FIG. 1, the server 104 is in communicationwith the population database 120 and the clickthrough database 122. Theserver 104 carries out a process by which the data in the two databases120, 122 are used to improve the search results provided in response toa search query 114 from a user 112 a.

Various methods in accordance with the present invention may be carriedout. One exemplary method according to the present invention comprisesreceiving a search query, determining a population associated with thesearch query, determining an article (such as a web page) associatedwith the search query, and determining a ranking score for the articlebased at least in part on data associated with the population. Thispopulation information is thus used to impact the ranking score for thearticle. Preferably, this population information indicates behavior ofthe population determined (e.g., a group or sub-group) relative to thesearch query and/or the article. For example, this information mayindicate the preferred articles selected by others in the same, similar,or related population in relation to the same, similar, or relatedquery.

A ranking score for a second article, a third article, a fourth article,etc., associated with the search query may also be determined based atleast in part on information associated with the population. Thesearticles may then be ranked against each other based on the rankingscore and presented in a ranked order to the person submitting thesearch query. Preferably, this results in a ranking that provides themost relevant articles to the user first.

The population associated with the query can be one or more of a varietyof populations. Examples include, but are not limited to demographicdata such as age, age range, sex, race, primary language, secondarylanguage, location, income, income range, a continent, a region, acountry, a state, a county, a city, a gender, an ethnic group, a group,persons with a shared characteristic, persons with a shared interest,persons grouped by a predetermined selection, and internet serviceprovider data (or the likely or possible data for any of these). Inother words, the population may be any group determined by anycharacteristics selected.

The population associated with the search query may be determined in oneor more of a variety of ways. For example, demographic data associatedwith a sender of the search query may be determined in order todetermine the population of interest, and this data can be any one ormore of the above-mentioned populations or others. For example, a likelygeographic location for the sender of the search query may be determinedby identifying the Internet Protocol address from which the search querywas sent, an address input by the sender to access a search engine, ordemographic data input by the sender. The population associated with thesearch query may also be determined in other ways, such as determiningdemographic data associated with the search query. This may beaccomplished by, for example, determining the language of the searchquery or determining data associated with previous senders of the searchquery.

As can be seen, determining a population associated with the searchquery can comprise determining self-identification data,automatic-identification data, or other data or information associatedwith a user transmitting the search query, such as user registrationdata, user preference data, and user selected data. For example, inregistering for membership or access to a web site, a user may inputregistration information. The user may express preferences during such aregistration process (e.g., preferred language) or may expresspreferences in other ways (such as in selection of web pages or domainuse). Automatic-identification data may comprise, for example, an IPaddress, a domain, default data obtained an application associated withthe user, or other automatically-procured or automatically-providedinformation. There can be, of course, some overlap betweenself-identification data and automatic-identification data.

Data associated with the population determined may itself by determinedin one or more of a variety of ways. For example, a selection score forthe article in a context of the population may be determined. As anillustration, it may be determined that a certain number of members ofthe population associated with the search query at hand had previouslyclicked on the article at issue. This selection score may indicate therelative interest of members of the population in the article at issue.

Again, there are a wide variety of data that can comprise the populationinformation. Other examples include a number of members of thepopulation, a number of members of the population that selected a resultreturned for the search query previously, a number of members of thepopulation that input the search query, a number of members of thepopulation to which search results for the search query were shown, atotal selection score, and a total number of members of the populationthat selected the article. There are many other examples.

In some instances, more than one populations associated with the searchquery may be determined and used in order to provide improved searchresults. In one embodiment, for example, a second population associatedwith the search query is determined, determining the ranking score forthe article is further based at least in part on data associated withthe second population.

These and other aspects of embodiments of the present invention aredescribed further herein. These exemplary aspects of embodiments of thepresent invention may be repeated or iterated to improve search results.Moreover, these and other steps taken in methods according to thepresent invention may be stored in the form of program code in acomputer-readable medium, such as memory associated with a processor, adisk, or other computer-readable medium.

FIGS. 2-3 illustrate an exemplary method 200 in accordance with thepresent invention in detail. This exemplary method is provided by way ofexample, as there are a variety of ways to carry out methods accordingto the present invention. The method 200 shown in FIG. 2 can be executedor otherwise performed by any of various systems. The method 200 isdescribed below as carried out by the system 100 shown in FIG. 1 by wayof example, and various elements of the system 100 are referenced inexplaining the example method of FIGS. 2-3. The method 200 shownprovides an improvement of a search ranking using populationinformation.

Each block shown in FIGS. '2-3 represents one or more steps carried outin the exemplary method 200. Referring to FIG. 2, in block 202, theexample method 200 begins. Block 202 is followed by block 204, in whicha population database 120 is provided. This may be accomplished by, forexample, constructing such a database or establishing communication withsuch a database. As described with reference to FIG. 1, the populationdatabase 120 stores population-type information for documents selectedin a search result for a search query 114 and other search queries.

Block 204 is followed by block 206, in which a selection database, inthis case a clickthrough database 122, is provided. This may beaccomplished by, for example, constructing such a database orestablishing communication with such a database. As described withreference to FIG. 1, the clickthrough database 122 stores dataassociated with users' clicks or inputs to a search result 132 providedby the search engine 124, such as a list of documents, such as webpages, provided in response to a search query 114 from a user 112 a.

Block 206 is followed by block 208, in which a search query, in the formof a search query signal is received by the server. In the embodimentshown, a user 112 a generates a search query 114 at a client device 102a. The client device 102 a transmits an associated search query signal126 reflecting the search query 114 to the server device 104 via anetwork 106. The search engine 124 receives the search query signal 126and processes the search query 114. For example, if the user 112 a typesa search query “boating” into the search or query field of a search pageon a browser application program, the client 102 a transmits a searchquery signal 126 that includes the text “boating” or some otherrepresentation or indication of “boating.” The search engine 124receives the signal 126 and determines that “boating” is the desiredsearch query 114.

Block 208 is followed by block 210, in which article data, in the caseshown, document data, is determined and received. In this block 210 inthe embodiment shown, the search engine 124 conducts a search forrelevant documents in a search database (not shown) or memory 118 thathave previously been indexed from the network 106. The search engine 124receives document data from the search database or memory 118 inresponse to the search query signal 126 reflecting the search query 114from the user 112 a. The document data is also referred to as theinitial search result for the search query 114. Document data caninclude, but is not limited to, a universal resource locator (URL) thatprovides a link to a document, web page, or to a location from which adocument or web page can be retrieved or otherwise accessed by the user112 a via the network 106. Note that document data is sometimes referredto as a “document” throughout the text of the specification.Alternatively, the document locator 134 obtains or otherwise receivesdocument data in response to a search query signal 126 reflecting asearch query 114.

For example, in block 210 shown, the search engine 124 shown woulddetermine a list of documents responsive to the search query “boating.”This list of documents would comprise the determined document data. Aninitial search result list for “boating” could comprise a list of 15documents. In the embodiment shown, this initial determination ofdocument data may be by means of a conventional search engine query andresults return.

Block 210 is followed by block 212, in which a population signal isdetermined for each article of interest. In the embodiment shown, thesearch engine 124 generates a population signal 128 for each document ofthe initial search result list determined in block 210 using apopulation function. For example, a population signal may be determinedfor each of the 15 documents identified in the initial search resultdetermined in block 210.

The population signal indicates a rating or score for the article ofinterest, and this rating or score reflects the relative interest ofthose in the same population group as the searching user. For example,articles previously selected by users in the same population group asthe querying user 112 a when carrying out the same query “Q” as input bythe user 112 a may receive a higher score for a population signal thanarticles previously selected only by users in population groups of whichthe user is not a member. This is but one example, however, and manyvariables and permutations may be used. This rating or score reflectedin the population signal may be used alone or in combination with otherscoring or rating signals to score or rank the document, and to rank andcompare groups of documents to, for example, provide a search result forthe query sent by the user.

In the embodiment shown, the population signal is determined by apopulation signal function. The population signal function may comprisean algorithm for calculating the population signal based on one or morevariables. The population signal function in the embodiment showncomprises a set of instructions processed by the population processor138. The algorithm is stored in memory 118.

Any one or more of a variety of population signal functions may beimplemented by various embodiments of the invention. Examples ofvariables that may be included in a population signal function include,without limitation, one or more of the following:

-   -   a total selection score for an article “d” for query “q,” (e.g.,        the total number of clicks by all users on document “d” returned        in search results in the context of query “q”, or the total        number of members of a population that selected a document “d”);    -   a selected or calculated weight of relationship between the        selection score for article “d” for a query “q” (e.g., the total        number of clicks by all users in population “pop” on the        document “d” returned in search results in the context of search        query “q.”);    -   a selection score for document “d” for query “q” in the context        of members of a population “pop” (e.g., a number of clicks for a        document by members of the population when the document is        returned for a search for query “q” );    -   a smoothing factor that reflects how much data is needed to        trust a click signal (e.g., a factor that reflects reliability        or trust in the number of clicks by all users on document “d”        returned in search results for query “q”);    -   a selection score for document “d” (e.g. number of clicks on        document “d”) for query “q” in the context of all users        regardless of a population;    -   a total selection score for a set of documents (e.g., the number        of clicks on all documents returned for query “q”) by all users        regardless of population;    -   a selection score for a set of documents (e.g., the number of        clicks on all documents returned for query “q”) by all users in        a population “pop”;    -   a total selection score for document “d” (e.g. number of clicks        on document “d”) by all users in a population “pop” for any        query “q_(i)”;    -   a selection score for document “d” (e.g. number of clicks on        document d”) by all users regardless of population for any query        “q_(i,)”;    -   a number of times a query “q” was input by users in a population        “pop”;    -   a number of times a query “q” was input by all users regardless        of population;    -   a number of members of a population (e.g., a number of members        of a population that input a particular search query, selected a        result returned for a particular search query, or were shown        search results for a particular search query);    -   one or more other ranking factors or scores, based on        population, the article under consideration, and/or other        factors.

There are a variety of other variables that may be included, and theseare only examples. Moreover, these and other variables may be limited ordefined by designated time period, a designated number of users, thenumber of users who are self-identified or automatically identified in apopulation, by those who input a query “Q,” or by other limitations orrefinements. Variables, limitations, definitions, or other dataassociated with population data are generally referred to as populationinformation or population data.

An example of a population signal function is as follows:

$\begin{matrix}{{S\left( {q,d_{j}} \right)} = \frac{{\# \left( {q,d_{i},P} \right)} + {\mu\left\lbrack \frac{\# \left( {q,d_{j}} \right)}{{\sum\limits_{i = l}^{N}\; {\# \left( {q,d_{j}} \right)}} + \mu} \right\rbrack}}{{\sum\limits_{i = l}^{N}\; {\# \left( {q,d_{i},P} \right)}} + \mu}} & (1)\end{matrix}$

wherein “S(q, d_(j))” is a score calculated for document “j” for asearch query “q,” based upon the population information and clickthroughdata for users of a particular population “P;”

“#(q, d_(j), P)” is the number of times document “d” was clicked byusers in population “pop” for search query “q;”

“μ” is a smoothing factor that reflects how much data is needed to trusta click signal such as the number of clicks by users of population “P”for a query “q;”

“#(q, d_(j))” is the number of rimes document “d” was clicked by allusers regardless of population for query “q;”

“#(q, d_(i))” is the total number of user clicks on document “i” forquery “q;” and

“#(q, d_(i), P)” is the total number of user clicks for all users ofpopulation “P” for document “i” for query “q.”

Another example of a population signal is as follows:

$\begin{matrix}{{S\left( {q,d_{j}} \right)} = \frac{{\# \left( {q,d_{i},P} \right)} + {\mu\left\lbrack \frac{\# \left( {q,d_{j}} \right)}{{\sum\limits_{i = l}^{N}\; {{SH}\left( {q,d_{i}} \right)}} + \mu} \right\rbrack}}{{\sum\limits_{i = l}^{N}\; {{SH}\left( {q,d_{i},P} \right)}} + \mu}} & (2)\end{matrix}$

wherein “SH(q,di)” is the number of times document “i” was shown forquery “q;”

“SH(q,di,P)” is the number of times document “i” was shown for query “q”for population “P;” and

the other variables are described with respect to example (1).

Another example of a population signal is as follows:

$\begin{matrix}{{S\left( {q,d_{j}} \right)} = \frac{{\# \left( {q,d_{j},P} \right)} + {\mu_{1}\left\lbrack \frac{\# \left( {q,d_{j}} \right)}{{\sum\limits_{i = l}^{N}\; {\# \left( {q,d_{i}} \right)}} + \mu_{2}} \right\rbrack}}{{\sum\limits_{i = l}^{N}\; {\# \left( {q,d_{i},P} \right)}} + \mu_{1}}} & (3)\end{matrix}$

wherein “μ₁” is a smoothing factor that reflects how much data is neededto trust a click signal such as the number of clicks by users ofpopulation “P” for a query “q;”

“μ₂” is a smoothing factor that reflects the how much data is needed totrust a click signal such as in the number of clicks by all users for aquery “q;” and

the other variables are described with respect to examples (1) and/or(2).

For purposes of illustration, the algorithm from example (1) is embodiedin the example method according lo the present invention shown in FIGS.2-3. Other algorithms besides the examples shown in (1), (2), and (3)may be used in accordance with the present invention, and algorithm (1)is provided to illustrate examples. Such other algorithms may containsome, all, or none of the variables shown in examples (1), (2), and (3).

FIG. 3 illustrates an example of a subroutine 212 for carrying out themethod 200 shown in FIG. 2 in accordance with example (1). Thesubroutine 212 shown provides a population signal 128 for each documentreceived in an initial search result 132. In other embodiments, thenumber of documents so analyzed may be limited to less than alldocuments received. An example of subroutine 212 is as follows.

Referring to FIG. 3, the example subroutine 212 begins at block 300. Atblock 300, a counter associated with the search engine 124 is set to avalue such as “1.” For example, the population processor 138 can set avariable “i” in an associated memory 118 to an initial value of “1.” Thecounter or variable “i” counts the number of documents that areprocessed by the subroutine 212, and the current value of “i” reflectswhich document in the list of documents in the document data is underoperation.

Block 300 is followed by block 302, in which another counter associatedwith the search engine 124 is set to a value such as “1.” For example,the population processor 138 can set a variable “j” in an associatedmemory 118 to an initial value of “1.” The counter or variable “j”counts the number of documents that are processed by the subroutine 212,and the current value of “j” reflects which document in the list ofdocuments in the document data is under operation.

Block 302 is followed by block 304, in which a population is determinedfor querying user 112 a. The search engine 124 determines a population,in this case “P.” For example, the search engine 124 determines apopulation associated with the user inputting the search query “Q.” Aspreviously described, self-identification data orautomatic-identification data, or a combination of both, can be utilizedby the search engine 124 to determine a population associated with theuser. In this embodiment, the search engine 124 determines from a user'sInternet Protocol address that the user is likely from France,designated as population “F,” therefore the search engine 124cross-references population information of users from France that havepreviously selected document “D₁” for the query “Q.” Upon determining apopulation “P” for the querying user, the search engine 124 can call tothe population database 120 to obtain any corresponding populationinformation as needed. For example, the search engine 124 can retrievedata from the population database 120 and determine that a total of “20”users in France, population “F,” have previously selected a particulardocument “D₁” for the search query “Q.” Therefore, populationinformation for a particular population “P” can be applied by the searchengine 124 to calculate a population signal for document “D₁” inaccordance with the present invention.

Block 304 is followed by block 306, in which a number of documents foranalysis is determined. In block 210, the server 104 received documentdata associated with the search query 114. Among the data determined wasthe total number of documents in the list of documents responsive to thesearch query 114.

This number of documents is characterized by (and is set as) thevariable “N.” For example, as mentioned earlier, a search result for thesearch query “boating” could have 15 documents, and the server 104 wouldset “N” to a value of “15.”

Note that in alternative embodiments, any total number of documents fora search query that has been defined or otherwise stored by thepopulation database 120 or another data storage device for a particularquery can be transmitted to, or otherwise determined by the searchengine 124 or population processor 138. Further note that the number ofdocuments for each search result for a particular search query candepend upon the population-type information previously stored in thepopulation database 120 as well as clickthrough data stored in theclickthrough database 122, or on other similar types of data stored inother data storage devices.

Block 306 is followed by block 308, in which a variable “M” isdetermined. The variable “M” reflects the number of documents for whicha population signal is determined. In most instances, “M” will have thesame value as the variable “N,” or the number of documents determined inblock 306 but it may be different. For example, as mentioned earlier, asearch result for the search query “boating” could have 15 documents,wherein the server set the variable N=15, and the server 104 would alsoset “M” to a value of “15.”

Block 308 is followed by block 310, in which a smoothing factor isdetermined. The search engine 124 determines a smoothing factor thatreflects how much data is needed to trust a click signal such as userclicks from users from a particular population for a particular query.For example, the population processor 138 utilizes a predeterminedequation or set of computer-executable instructions to determine thesmoothing factor that accounts for how much data is needed to trust aclick signal or the quality of the user clicks from all users and fromusers from a particular population for a particular query. The smoothingfactor can be particularly useful if there are very few user clicks fromusers of a particular population or if user clicks from a particularsource is known or otherwise perceived not to be reliable or otherwisetrustworthy. In that case, the smoothing factor can be set to a constantvalue or a value that can otherwise influence the weight or value of thedata associated with a particular population. In most instances, thesmoothing factor is applied to a population signal function or to a setof computer-executable instructions processed by the populationprocessor 138.

As applied to an example, for the query “boating,” the populationprocessor 138 determines a smoothing factor if there is an insufficientnumber of user clicks from users in France. Thus for the example in thetable above, if query “Q” is “boating” and 20 user clicks from users inFrance is not a sufficient number of clicks to rely upon, then asmoothing factor is determined. The smoothing factor is represented by“μ” in the population signal function above in subroutine 212. Thisfactor indicates the reliability or perceived trust in the number ofclicks by users in France to the search query “boating.”

As applied to another example, for the query “cricket” by a user inFrance, the population processor 138 determines a smoothing factor ifthere is an insufficient number of user clicks from users in France.However, since France is a sub-population of “Europe,” the populationprocessor 138 can use click information corresponding to users in“Europe” to augment the click information for the query “cricket” fromthe user in France. Generally, if click information for a sub-populationis sparse or does not exist, information from a higher population levelcan be used to augment the click information. In some instances, theremay be additional levels of populations and sub-populations that couldbe used in this manner, i.e., “Parisians” are a sub-population to“France” which is a sub-population to “Europe” which is a sub-populationto the “World.”

Note that in some instances, a general smoothing factor can bedetermined. The search engine 124 determines a general smoothing factorthat reflects the reliability or trust in user clicks by all users for aparticular document for a particular query. In the example shown, anassumption is made that the reliability or trust in user clicks from thelocal population is the same as the reliability or trust of user clicksfrom the general population, and the smoothing factor as determinedabove can be used as the general smoothing factor.

By way of further example, a general smoothing factor can be determinedas follows. The population processor 138 accesses the populationdatabase 120 or other data storage device to retrieve user click data.Using a predetermined equation or set of computer-executableinstructions, the population processor 138 determines the number of userclicks by all Users for all documents in a search result for aparticular query. The general smoothing factor can then be set as thenumber of clicks or selections by all users for all documents in asearch result for a particular query.

If smoothing factors, values, or scores for a particular document for aparticular query have previously been stored in the population database120, the population processor 138 retrieves the smoothing factors,values, or scores for a particular query. For example, the populationdatabase 120 may indicate that there is an insufficient number of clicksfrom all users for a document in a search result for the search query“boating,” such as in the table above where only one user from Japanselected a document “D₁” for the query “Q.” In this instance, the totalnumber of clicks from all users for all documents in the search resultfor the search query “boating” should be used. A general populationsmoothing factor as determined above can then be used in an algorithm togradually transition between the two results. A determination can bemade whether to use the number of clicks from all users for a documentin a search result for the search query “boating,” or the total numberof clicks from all users for all documents in the search result for thesearch query “boating” so that improved search results can be obtainedfrom population information and selection data.

Block 310 is followed by block 312, in which a number of selections, inthis case clicks, by a particular population “P” is determined for thecurrent document of interest (document “i”) for a particular query(“Q”). The search engine 124 determines the number of clicks fordocument “i” when document “i” is returned in search results for query“Q.” For example, the population processor 138 accesses populationinformation stored by the population database 120 or other data storagedevices as well as clickthrough data stored by the clickthrough database122 or previously shared with the population database 120. Thepopulation processor 138 applies a predetermined equation or set ofcomputer-executable instructions to some or all of the populationinformation and clickthrough data to determine the number of clicks by apopulation on a document for a search query, also referred to as“#(q,d_(i),P).” For example, if the query “Q” is “boating,” then it canbe determined that there were 20 total user clicks by users in theFrance on document “D₁” for query “Q.”

Block 312 is followed by block 314, in which a number of selections, inthis case clicks, by all users is determined for the current document ofinterest (document “i”) for a particular query (“Q”). The search engine124 determines the number of clicks by all users for document “i” whendocument “i” is returned in search results for query “Q.” For example,the population processor 138 accesses population information stored bythe population database 120 or other data storage devices as well asclickthrough data stored by the clickthrough database 122 or previouslyshared with the population database 120. The population processor 138applies a predetermined equation or set of computer-executableinstructions to some or all of the population information andclickthrough data to determine the number of clicks by all usersregardless of population on a document for a search query, also referredto as “#(q,d_(i)).” For example, if the query “Q” is “boating,” then itcan be determined that there were 101 total user clicks by all usersregardless of population on document “D₁” for query “Q.”

Block 314 is followed by decision block 316, in which a decision is madewhether all documents for a search query have been processed. The searchengine 124 compares the counter or variable “i” initially set at a valueof “1” in block 300 to the variable “N,” which has been set to a valuein block 306 according to the number of documents to be processed forthe search query “Q.” If not all the documents have been processed, thenthe counter or variable “i” will hot equal the variable “N” and the “NO”branch is followed to block 318. In alternative embodiments, a maximumnumber of documents for analysis may be set. For example, “N” may be setto a maximum number that is less than the number of documents determinedin block 306.

In block 318, a counter is incremented to track the number of documentsthat have been processed. For example, the counter or variable “i”initially set at a value of “1” is incremented to a next value such as“2.” The subroutine 212 then returns to block 312 to continue processingthe next document. Subsequent documents are processed by blocks 312-314,and the counter or variable “i” at block 318 is subsequently incrementeduntil all of the documents are processed, and the value of the counteror variable “i” equals “N.” Thus, in the example provided previously for“boating,” all 15 documents of the search result for the search query“boating” would be processed by blocks 312-314.

When all of the documents have been processed, the “YES” branch isfollowed from decision block 318, and the subroutine 212 continues atblock 320.

At block 320, a sum of the number of selections, in this case clicks, bya particular population “P” for all documents (documents “i”) for aparticular query (“Q”), and a sum of the number of selections, in thiscase clicks, by all users for all documents (documents “i”) for aparticular query (“Q”) are determined. In the embodiment shown, thesearch engine 124 determines a sum of the number of selections, in thiscase clicks, by a particular population “P” for all documents (documents“i”) for a particular query (“Q”) that describes the total number ofclicks by the particular population “P” on documents associated with asearch query “Q.” For example, the population processor 138 determines asum which reflects some or all user clicks from a particular population“F” on documents returned in response to a prior search for a searchquery “Q.” The sum can then be applied by the search engine 124 orpopulation processor 138 to a population signal function or to set ofcomputer-executable instructions. Note that the number of selectionsdetermined in block 312 is summed by block 320 for all documents “i.”

Furthermore, the search engine 124 determines a sum of the number ofselections, in this case clicks, by all users for all documents(documents “i”) for a particular query (“Q”) that describes the totalnumber of clicks by all users regardless or population on documentsassociated with a search query “Q.” For example, the populationprocessor 138 determines a sum which reflects some or all user clicksfrom all users regardless of population on documents returned inresponse to a prior search for a search query “Q.” The sum can then beapplied by the search engine 124 or population processor 138 to apopulation signal function or to set of computer-executableinstructions. Note that the number of selections determined in block 314is also summed by block 320 for all documents “i.”

Block 320 is followed by block 322, in which a smoothing factor isdetermined. The search engine 124 determines a smoothing factor “μ” thatreflects how much data is needed to trust a click signal such as userclicks from users from a particular population for a particular query.For example, the population processor 138 utilizes a predeterminedequation or set of computer-executable instructions to determine thesmoothing factor that accounts for how much data is needed to trust aclick signal such as the user clicks from all users and from users froma particular population for a particular query. The smoothing factor canbe particularly useful if there are very few user clicks from users of aparticular population or if user clicks from a particular source isknown or otherwise perceived not to be reliable or otherwisetrustworthy. For example, as applied to the example for the query “Q” inthe table above, since there is only a single click from users in Japanon document “D1” for the query “Q,” the smoothing factor “μ” can be setto a constant value such as “10.” In this case, the smoothing factor canbe used to affect or otherwise influence the weight or value of the dataassociated with a particular population. In most instances, thesmoothing factor is applied to a population signal function or to a setof computer-executable instructions processed by the populationprocessor 138.

Note that in some instances, a general smoothing factor can bedetermined. The search engine 124 determines a general smoothing factorthat reflects how much data is needed to trust a click signal such asuser clicks by all users for a particular document for a particularquery. In the example shown, an assumption is made that the amount ofdata needed to trust user clicks from the local population is the sameas the amount of data needed to trust user clicks from the generalpopulation, and the smoothing factor as determined above can be used asthe general smoothing factor.

By way of further example, a general smoothing factor can be determinedas follows. The population processor 138 accesses the populationdatabase 120 or other data storage device to retrieve user click data.Using a predetermined equation or set of computer-executableinstructions, the population processor 138 determines the number of userclicks by all for a particular document for a particular query.

If smoothing factors, values, or scores for a particular document for aparticular query have previously been stored in the population database120, the population processor 138 retrieves the smoothing factors,values, or scores for a particular query. For example, the populationdatabase 120 may indicate that there is an insufficient number of clicksfrom all users for a document in a search result for the search query“boating,” and that the total number of clicks from all users for alldocuments in the search result for the search query “boating” should beused. A general population smoothing factor can gradually transitionbetween the two results. A determination can be made whether to use thenumber of clicks from all users for a document in a search result forthe search query “boating,” or the total number of clicks from all usersfor all documents in the search result for the search query “boating” sothat improved search results can be obtained from population informationand selection data.

Block 322 is followed by block 324, in which a number of selections, inthis case clicks, by all users for each document (document “j”) returnedin a search result for a particular query (“Q”). The search engine 124determines the number of clicks for document “j” regardless ofpopulation when document “j” is returned in search results for query“Q.” For example, the population processor 138 accesses populationinformation stored by the population database 120 or other data storagedevices as well as clickthrough data stored by the clickthrough database122 or previously shared with the population database 120. Thepopulation processor 138 applies a predetermined equation or set ofcomputer-executable instructions to some or all of the populationinformation and clickthrough data to determine the number of clicks by apopulation on a document for a search query, also referred to as“#(q,d_(j)).” For example, if the query “Q” is “boating,” then it can bedetermined that there were 101 total user clicks by all users ondocument “D_(i)” for query “Q.”

Block 324 is followed by block 326, in which a number of selections, inthis case clicks, by all users in a population “P” is determined foreach document (document “j”) returned in a search result for aparticular query (“Q”). The search engine 124 determines the number ofclicks by all users in a population “P” for document “j” when document“j” is returned in search results for query “Q.” For example, thepopulation processor 138 accesses population information stored by thepopulation database 120 or other data storage devices as well asclickthrough data stored by the clickthrough database 122 or previouslyshared with the population database 120. The population processor 138applies a predetermined equation or set of computer-executableinstructions to some or all of the population information andclickthrough data to determine the number of clicks by a population on adocument for a search query, also referred to as “#(q,d_(j),P).” Forexample, if the query “Q” is “boating,” then it can be determined thatthere were 20 total user clicks by all users in the population of Franceon document “D₁” for query “Q.”

Block 326 is followed by block 328, in which a population signal for adocument for a particular search query is determined. The search engine124 determines a population signal 128 for a particular document in asearch result 132. For example, the population processor 138 uses anumber of factors such as the number of times a search query was askedby users of a particular population; the number of times a search querywas asked by all users; the number of times a search query was asked anda particular document was clicked by users of a particular population;the number of times a search query was asked and a particular documentwas clicked by users of all populations; the number of times a documentwas clicked by users of a particular population for any search query;the number of times a document was clicked by all users for any searchquery; the smoothing factor, if needed; and the population click weight,if needed, to determine a population signal 128 for a particulardocument in a search result.

In the embodiment shown, this population signal 128 is calculated usingthe data determined in previous blocks 300-324 discussed and thealgorithm shown in example (1). As applied to the prior example for thequery “boating,” the population processor 138 determines a populationsignal 128 for a particular document in a search result 132. Asrepresented by “S(Q,D₁)” in the population signal function as shownabove in subroutine 212, a weighted value representing the weightedtotal number of user clicks on document “j” after counting clicks byusers of a particular population “F” is determined by the populationprocessor 138. This is carried out by performing the mathematicalfunctions as indicated by the algorithm described above to calculate theS_(j)(Q,D_(j))” for document “j.”

Block 328 is followed by decision block 330, in which a decision is madewhether all documents for a search query have been processed. The searchengine 124 compares the counter or variable “j” initially set at a valueof “1” in block 302 to the variable “M,” which has been set to a valueaccording to the number of documents to be processed for the searchquery. If all the documents have been processed, then the counter orvariable “j” will equal the variable “M” and the “YES” branch isfollowed to block 332. In alternative embodiments, a maximum number ofdocuments for analysis may be set. For example, “M” may be set to amaximum number that is less than the number of documents determined inblock 302.

In block 332, the subroutine 212 ends.

If however in decision block 330, not all of the documents have beenprocessed and the counter or variable “j” is not equal to the variable“M,” then the “NO” branch is followed to block 334.

In block 334, a counter is incremented to track the number of documentsthat have been processed. For example, the counter or variable “j”initially set at a value of “1” is incremented to a next value such as“2.” The subroutine 212 then returns to block 324 to continue processingthe next document. Subsequent documents are processed by blocks 324-328,and the counter or variable “j” at block 334 is subsequently incrementeduntil all of the documents are processed, and the value of the counteror variable “j” equals “M.” Thus, in the example provided previously for“boating,” all 15 documents of the search result for the search query“boating” would be processed by blocks 324-328.

When all of the documents have been processed, the “YES” branch isfollowed from decision block 332, and the subroutine 212 ends at block332.

Referring again to FIG. 2, subroutine 212 is followed by block 214, inwhich the population signal for each document is provided to the rankingprocessor. For example, in the embodiment shown, the calculated score“S(Q,D_(j))” for each document “1-N” would be included in “N” populationsignals. The population signal 128 for each document is transmitted tothe ranking processor 136 for determining subsequent rankings or scoresof search results in response to other search queries. The rankingprocessor 136 includes a ranking or scoring function or set ofcomputer-executable instructions that incorporates the population signal128 and/or other output from the population processor 138. For example,a weighted value generated from subroutine 212 is transmitted to theranking processor 136, which utilizes a population signal 128 such as aweighted value to rank or otherwise score subsequent search results.Other signals 130 generated for each document by the search engine 124or another system or method can also be transmitted to the rankingprocessor 136 to rank or score subsequent search results.

Block 214 is followed by block 216, in which search results areprovided. The ranking processor 136 generates a ranking or scoring ofeach document located in a search result 132 in response to a searchquery 114. Using the population signal 128 from block 214, such as aweighted value, the ranking processor 136 affects the ranking or scoringof one or more documents located in a search result 132. Note that theranking processor 136 can use other signals such as those shown in FIG.1 as 130 in conjunction with the population signal 128 to rank orotherwise score documents of a search result 132. In some instances, theranking processor 136 can further decide whether to utilize a particularpopulation signal 128 and/or other signals 130 during processing of ascore or ranking for a search result 132.

Block 216 is followed by block 218, in which the method 200 ends.

In other embodiments of the invention, the method 200 can be utilized inan iterative manner to determine a new or updated population signalwhenever new or changes to data in the population database 120 and/orclickthrough database 122 or other data storage devices is received orotherwise obtained. When a new or updated population signal isdetermined, the signal can then be transmitted to the ranking processor136 to change or to update the ranking or scores for a search result132.

While the above description contains many specifics, these specificsshould not be construed as limitations on the scope of the invention,but merely as exemplifications of the disclosed embodiments. Thoseskilled in the an will envision many other possible variations that arewithin the scope of the invention.

1. A method for determining a search ranking score, comprising: (a)receiving a search query; (b) determining a first population associatedwith the search query; (c) determining a first article associated withthe search query; and (d) determining a first ranking score for thefirst article based at least in part on data associated with the firstpopulation.
 2. The method of claim 1, wherein determining the firstpopulation associated with the search query comprises determining ademographic data associated with a sender of the search query.
 3. Themethod of claim 2, wherein determining the demographic data associatedwith the sender comprises determining a likely geographic location forthe sender.
 4. The method of claim 3, wherein determining the likelygeographic location for the sender comprises determining at least one ofthe following: the Internet Protocol address from which the search querywas sent; an address input by the sender to access a search engine; anddemographic data input by the sender.
 5. The method of claim 2, whereindetermining the demographic data for the sender comprises determining atleast one of the following: age, age range, sex, race, primary language,secondary language, location, income, income range, a continent, aregion, a country, a state, a county, a city, a gender, an ethnic group,a group, persons with a shared characteristic, persons with a sharedinterest, persons grouped by a predetermined selection, and internetservice provider data.
 6. The method of claim 1, wherein determining thefirst population associated with the search query comprises determininga demographic data associated with the search query.
 7. The method ofclaim 6, wherein determining the demographic data associated with thesearch query comprises at least one of the following: determining thelanguage of the search query; and determining data associated withprevious senders of the search query.
 8. The method of claim 1, whereindetermining the first population associated with the search querycomprises determining a self-identification data associated with a usertransmitting the search query.
 9. The method of claim 8, wherein theself-identification data is selected from at least one of the following:user registration data, user preference data, and user selected data.10. The method of claim 1, wherein determining the first populationassociated with the search query comprises determining anautomatic-identification data associated with a User transmitting thesearch query.
 11. The method of claim 10, wherein theautomatic-identification data comprises at least one of the following:an IP address, a domain, and default data obtained an applicationassociated with the user.
 12. The method of claim 1, wherein the dataassociated with the first population comprises a selection score for thefirst article in a context of the first population.
 13. The method ofclaim 12, wherein the selection score for the first article in thecontext of the first population comprises a number of clicks for thefirst article by members of the first population.
 14. The method ofclaim 1, wherein the data associated with the first population comprisesa number of members of the first population.
 15. The method of claim 14,Wherein the number of members of the first population comprises a numberof members of the first population that selected a result returned forthe search query.